Performance of four sea surface temperature assimilation schemes in the South China Sea |
| |
| Authors: | Yeqiang Shu Jiang Zhu Dongxiao Wang Changxiang Yan Xianjun Xiao |
| |
| Institution: | 1. LED, South China Sea Institute of Oceanology, Chinese Academic of Science, Guangzhou, China;2. LAPC, Institute of Atmospheric Physics, Chinese Academic of Science, Beijing, China;3. Laboratory for Climate Studies, China Meteorological Administration, Beijing, China;1. Institute of Marine Science and Technology, Dokuz Eylul University, Izmir, Turkey;2. Centro Euro-Mediterraneo per i Cambiamenti Climatici, Viale Aldo Moro 44, Bologna, Italy;3. Istituto Nazionale di Geofisica e Vulcanologia, Viale Aldo Moro 44, Bologna, Italy;4. Department of Physics and Astronomy, University of Bologna, Italy;1. Research Institution of Combining Chinese Traditional and Western Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu 225001, China;2. Department of Endocrinology, Clinical Medical College, Yangzhou University, Yangzhou, Jiangsu 225001, China;3. Department of Physiology, Nanjing University of Chinese Medicine Hanlin College, Taizhou, Jiangsu 225300, China;4. Taizhou Hospital of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Taizhou, Jiangsu 225300, China;1. Department of Chemistry and Center of Excellence for Innovation in Chemistry, Faculty of Science, Mahasarakham University, Mahasarakham 44150, Thailand;2. Program of Science, Faculty of Science and Technology, Bansomdejchaopraya Rajabhat University, Bangkok 10600, Thailand;3. Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand;4. Division of Laser Biochemistry, Institute for Laser Technology, Osaka 550-0004, Japan;5. Department of Applied Chemistry and Bioengineering, Graduate School of Engineering, Osaka City University, Osaka 558-8585, Japan;1. Department of Psychological and Brain Sciences and the Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, California;2. Department of Behavioral Neuroscience, Oregon Health & Science University and Portland Alcohol Research Center, Veterans Affairs Portland Healthcare System, Portland, Oregon;3. Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland.;1. School for Marine Science and Technology, University of Massachusetts Dartmouth, 706 South Rodney French Blvd, New Bedford, MA 02744, United States;2. Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States;3. The Graduate School of Oceanography, University of Rhode Island, 215 South Ferry Road, Narragansett, RI 02882, United States;4. Woods Hole Coastal and Marine Science Center, U.S. Geological Survey, 384 Woods Hole Road, Woods Hole, MA 02543, United States;5. Department of Earth, Atmospheric & Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Building 54-1810, Cambridge, MA 02139, United States;6. International Center for Marine Studies, Shanghai Ocean University, Shanghai 201306, PR China |
| |
| Abstract: | Four existing sea surface temperature (SST) assimilation schemes are evaluated in terms of their performances in assimilating the advanced very high resolution radiometer pathfinder best SST data in the South China Sea using the Princeton Ocean Model. Schemes 1 and 2 project SST directly to subsurface according to model-based correlations between SST and subsurface temperature. The difference between these two schemes is related to the order of vertical projection and horizontal optimal interpolation (OI). In Scheme 1, the spatially non-uniform SST observations are first projected to subsurface levels, followed by horizontal OI at each level. While in Scheme 2, the remotely sensed SSTs are first optimally interpolated to all grid points at the surface, followed by projecting gridded SSTs to subsurface levels. Scheme 3 assumes that the mixed layer is well mixed and has a uniform temperature vertically. In Scheme 4, SST is propagated to subsurface levels using a linear relationship of temperature between any two neighboring depths (Scheme 4a) or between surface and subsurface (Scheme 4b), which is derived by empirical orthogonal function (EOF) technique. To verify the results of the four schemes, the authors use the hydrographic data from two cruises during the South China Sea Monsoon Experiment in April and June 1998. It was shown that all four schemes could improve the SST field by reducing about 50% of the root mean square errors (RMSEs). All but Scheme 3 can improve model thermocline structure that is too diffused otherwise, though the RMSEs increase in the thermocline, especially for Scheme 2 when the model has opposite bias between upper layers and lower layers. Scheme 3 fails in the subsurface depth by increasing the thermocline depth, especially when there is a cold model bias. Projecting SST downward by EOF technique can deepen the depth of assimilation especially in Scheme 4a. Both Schemes 4a and b can correct the bias in the mixed layer and do not change the vertical thermal structure. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
